AI agents: what they are and how they differ from bots

AI agents are language model programs that are given a goal. The agent decides for itself what steps are needed, accesses tools, checks the result, and stops when the goal is achieved or when a human needs to take over. A chatbot replies to messages. An agent performs a task. The difference sounds like a play on words until you look at an evening shift at a clothing store that sells via Instagram and a website. We will use this exact example to break it down.

What are AI agents in simple terms

At the heart of any agent are four things. A language model that understands text and formulates responses. Tools that the model has access to: a product catalogue, an inventory spreadsheet, a CRM, an appointment calendar, a payment form. Context, meaning the conversation history and customer data. And boundaries: a list of what the agent is allowed to do on its own and what needs to be handed over to a human.

Here is what it looks like in a clothing store. At 22:15, a customer writes in direct messages: “Do you have this dress in M? I'm between M and L, height 172.” A script-based bot would have offered a menu here. An assistant without tools would have replied with general words about the size chart. An agent acts differently: it opens the size chart for this specific model, compares it with the measurements, checks stock levels, sees that M is available and L is running out, and writes: “For your height, M is better, the length will be to the mid-knee. M is in stock, only one L left.” Next, it asks for the branch address and creates an order in the CRM. In the morning, the manager sees a ready-made order.

In December 2024, Anthropic described this distinction in technical terms. It defines a workflow as a system in which “language models and tools are orchestrated through predefined code paths,” and an agent as a system in which “the model itself directs its own process and tool use” (Anthropic, Building effective agentsThat same text advises starting with the simplest option and only making it more complex when that measurably improves the result. The advice is useful not only to developers. If your clients are asking five identical questions, you don't need an agent yet; an FAQ will suffice.

How AI agents differ from chatbots and assistants

Three words are often used as synonyms, and service providers take advantage of this. Let's distinguish them.

	Scripted chatbot	AI assistant	AI agent
What does it react to	on the button or given word	to any text	on purpose
Actions after the response	Nothing	Nothing	action: views data, creates record, reminds
Where does he get knowledge	from the script	from the model and what they gave him	from the tools and knowledge base
When it stops	after the branch	after the reply	when the task is completed or a human is needed
Example	Delivery / Payment menu“	fabric composition reply	order in CRM with selected size

We wrote separately about the first column in the article What is a chatbot. One thing is important here: the assistant and the agent can run on the same model. What sets them apart is access to tools and the right to use them, while the level of “intelligence” in both can be the same.

Autonomous AI agents: how much freedom to give

The word “autonomous” sounds like a promise that everything will happen by itself. The market has already tested this promise for itself. According to a Gartner forecast from 25 June 2025, over 40 % of agentic AI projects will be cancelled by the end of 2027 due to rising costs, unclear business value, or insufficient risk control. A survey of 3,412 webinar participants in January 2025 showed that 19 % of organisations have made significant investments in agents, 42 % are being cautious, and 31 % are still watching and waiting (Gartner, 25/06/2025).

In the same release, Gartner analyst Anushri Verma offers a rule worth pinning above your desk: agents are needed where decisions have to be made; automation is enough for routine tasks; an assistant is sufficient for simple information retrieval. There is also a forecast for 2028: 15 % of daily work decisions will be made autonomously, up from zero in 2024, and 33 % of enterprise software will contain agentic AI, up from less than 1 %. Two months later, Gartner clarified the pace: by the end of 2026, 40 % of enterprise applications will have built-in task-specific agents, compared to less than 5 % in 2025 (Gartner press release dated 26 August 2025).

For the shop, this translates to a short list of boundaries. The agent itself answers questions about availability and size, fabric composition, and delivery, and places the order itself at the catalogue price. A discount exceeding a set percentage, a return, a complaint, or any question not covered by the knowledge base are escalated to a human along with the entire dialogue history. Agent autonomy is the ability to recognise for itself where its remit ends.

AI agents for business: where they are already working

In small businesses, agents work wherever the customer writes: in Instagram direct messages, on Telegram, or in the website widget. Product enquiries arrive right there, and replies are expected within minutes.

Hence three typical use cases for an agent. First: direct and Telegram sales, meaning availability, size, price, and processing. Second: booking appointments in clinics and salons, where the agent sees free slots in the calendar, suggests a time, books the appointment and sends a reminder the day before. Third: post-purchase support, ranging from “where is my parcel” to “will the second item go with the first”. In each of the three scenarios, the agent handles repetitive dialogues, while a human takes over those where a decision is required.

One working evening of an agent in a clothing store

Let's go back to the clothes shop and look at the evening minute by minute.

22:15. Question about a dress in M. The agent reads the size chart and stock levels, replies in 10–30 seconds, as claimed for Marichka AI. Suggests M, confirms the delivery branch, creates an order in the CRM.

22:40. “Is there anything similar, but with sleeves?” The agent searches the catalogue by attribute, showing two options with prices. The female client takes a pause.

23:30. The client didn't reply. The agent sets a reminder to return to the conversation tomorrow afternoon: this is the very follow-up that busy managers most often forget about.

23:50. Another message: “Ordered a week ago, the dress is too small, I want to return it.” The return is outside the agent's scope. He replies that he is passing the matter on to the manager, and in the morning she sees the chat with the entire order history.

How many such evenings end in an order depends on the niche and the average order value: in cosmetics and food delivery, this share can differ multiple times over, so it is worth calculating it based on your own chats for the month. The mechanics are the same everywhere: a fast first response, data from the catalogue, and an order placed without human involvement. How the journey from the first message to the order is structured and how to calculate the return on investment, we broke down in a separate article about Sales AI agent.

How to tell an agent from a renamed bot

In the same 2025 release, Gartner coined the term “agent washing”: assistants, RPAs and chatbots are being rebranded as agents without any agentic capabilities, and out of thousands of vendors, the analysts confirm such capabilities in approximately 130. A demo check takes ten minutes.

1. Поставте питання, якого немає в жодному FAQ, з двома умовами одразу: “сукня до двох тисяч гривень, щоб була в наявності в M і приїхала до п’ятниці в Одесу”.
2. Попросіть оформити замовлення і подивіться, чи з’явилося воно у вашій CRM або хоча б у таблиці.
3. Напишіть щось поза межами: попросіть знижку в сорок відсотків. Агент має відмовити і передати питання людині.
4. Замовкніть на хвилину. Агент, який уміє продавати, повернеться до вас сам.

Якщо хоч на одному кроці у відповідь прийшло меню з кнопками, ви дивитеся на бота з новою назвою. Той самий тест варто провести і на власних діалогах: візьміть останні тридцять розмов з клієнтами й позначте ті, де менеджерка щось додатково перевіряла чи оформлювала після відповіді – дивилася залишки, створювала замовлення, нагадувала клієнту. Це і є навантаження, яке можна віддати агенту. Якщо таких діалогів менше п’яти, вистачить хорошого FAQ; якщо більше половини, наступна розмова варта того, щоб окреслити межі: що агент бере на себе сам, а що завжди йде до людини.